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In this paper, a framework for nonlinear model predictive control (NMPC) for heavily noise-affected systems is presented. Within this framework, the noise influence, which originates from uncertainties during model identification or measurement, is explicitly considered. This leads to a significant increase in the control quality. One part of the proposed framework is the efficient state prediction,...
Model identification and measurement acquisition is always to some degree uncertain. Therefore, a framework for nonlinear model predictive control (NMPC) is proposed that explicitly considers the noise influence on nonlinear dynamic systems with continuous state spaces and a finite set of control inputs in order to significantly increase the control quality. Integral parts of NMPC are the prediction...
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